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Re: st: using post stratification weights

From   Stas Kolenikov <>
Subject   Re: st: using post stratification weights
Date   Mon, 13 Feb 2012 12:52:08 -0500

On Mon, Feb 13, 2012 at 11:36 AM, Afif Naeem <> wrote:
> The survey code-book does not provide much information with regards to design-stratification. But they do tell me that they used some combination of random digit dialing (RDD) sampling and address-based sampling (ABS) methodology. I have a feeling that they did not used design-stratification for sampling purposes.

If they obtained a part of the sample from RDD frame, and another part
from ABS frame, then these are two independent strata, and should be
accounted as such. You'd have to continue clarifying this.

> My main concern is the low response/completion rate of the survey i.e. 62.5% of respondents do actually complete the survey. Would using the post-stratification (i.e. Raking) weights without mentioning any post-strata correct for any bias that may arise due to low response rate? And where/how would the variable used (mentioned below) used in the Raking process would come into play? (assuming if the do come into play)

If the response is MAR with the variables determining non-response
used in the non-response model that led to the post-stratification
adjustments (i.e., age, gender, etc.), then you will be fine. But this
is a strong assumption to make.

> Moreover, the post-stratification weight variable provided in the data set ranges from a value of 0.13 to 5.6, with a mean value of 1.000075. As far as I understand, pweight is the inverse of sampling fraction and hence should be greater than (or equal to) 1. Do I need to worry about it or STATA will adjust for it?

Stata will not make any guesses; if you specified these weights, Stata
will use them, and does not care whether they sum up to the total
population size (as they should) or to the sample size (which is a
shortcut for SAS or SPSS that can't do things otherwise). It is up to
the analyst to specify the weights appropriately and interpret the
results. If you don't need to estimate the population totals (total
income; total # of events; etc.), then you can get along with these

> Lastly, how precise it is to use post-stratification weights in Bivariate Logit Model. My results completely flip-over and loose statistical significance when I use weights in the model using survey commands. Signs and statistical significance can not be justified on the basis of any (economic) theory. However, when I dont use weights, the results come out as expected. I wonder if I am doing something wrong here. Can the use of weights change the parameter estimates and average marginal effects to such an extent?

-svy postestimation- command provide design effects, i.e., the ratio
of variances of design-based estimates vs. SRS estimates. I wouldn't
be surprised if your poststratification has actually increased the
variances quite a bit. Unfortunately, that's the price you have to pay
to get design-consistent estimates.

Stas Kolenikov, also found at
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